Influence of the ratio on the mechanical properties of epoxy resin composite with diapers waste as fillers for partition panel application

Materials play significant role in the domestic economy and defense with the fast growth of science and technology field. New materials are the core of fresh technologies and the three pillars of modern science and technology are materials science, power technology and data science. The prior properties of the partition panel by using recycled diapers waste depend on the origin of waste deposits and its chemical constituents. This study presents the influence of the ratio on the mechanical properties of polymer in diapers waste reinforced with binder matrix for partition panel application. The aim of this study was to investigate the influence of different ratio of diapers waste polymer reinforced epoxy-matrix with regards to mechanical properties and morphology analysis. The polymer includes polypropylene, polystyrene, polyethylene and superabsorbent polymer (SAP) were used as reinforcing material. The tensile and bending resistance for ratio of 0.4 diapers waste polymers indicated the optimum ratio for fabricating the partition panel. Samples with 0.4 ratios of diapers waste polymers have highest stiffness of elasticity reading with 76.06 MPa. A correlation between the micro structural analysis using scanning electron microscope (SEM) and the mechanical properties of the material has been discussed

A reference model for information specification for metalworking SMEs

The work reported in this thesis offers a novel basis for the realisation of specifications for information requirements to meet the distinct operational requirements of metalworking SMEs. This has been achieved through the development of a reference SME enterprise model based on fundamental ideas of the holon and fractal factory concepts. The novel concept of a node holon is introduced, which allows the representation of the human dominated interactions in a company based on the fundamental concepts of the holon. This offers a competitive alternative to the methods for enterprise modelling and information specification which are based solely around business processes and procedural rules. A new representation for the organisation of the SME has been based on identifying the major zones of activity within the enterprise, which is seen to provide a more appropriate representation for companies whose basis for operation is informally structured. Two classes of zones have been identified, these are the business support zone and manufacturing zone. The relationship between a top down description of the enterprise as zones and the complementary bottoms up modelling of the enterprise based on concepts of the node holon are described in detail. A critical study of two candidate modelling architectures, namely CIN40SA and ARIS will show the applicability of the individual architectures for the task information specification. The constituents of the SMEE enterprise reference model is placed within the context of contemporary enterprise modelling practice by mapping against one of the architectures. This will demonstrate how the architectures can readily accommodate new modelling approaches whilst retaining their major advantages, thereby increasing their applicability and potential uptake. The reference SME enterprise model has been readily applied in the study of an SME, where a representation of the company has been achieved solely on the current organisation of its business support and manufacturing activities. The holonic aspects of the enterprise have also been successfully modelled. This process is supported by a CASE tool which has it constructs underpinned by the reference SME enterprise model

Systems Engineering Approach to the Automated Manufacture of Static Tool-Holding

Investigation of the ‘design to delivery’ phase is critical for establishing the productivity of a small manufacturing enterprise producing small, variable product batches. Previous research indicates how productivity of small manufacturing enterprises is subject to multiple challenges including reduced utilisation of advanced technology, having limited resources and needing the correct culture. Existing studies of productivity problem-solving have used a typical structure of: 1) problem definition through existing practice analysis, 2) identification of areas to improve and 3) use of analytical tools to rank productivity losses or ‘bottlenecks’. Stakeholder input (often through interviews or group exercise) and quantitative analysis tools are key aspects of most studies, with them used in combination to determine the largest bottlenecks. While existing works have successfully identified key bottlenecks within organisations there are a lack of case studies which present the process of solution implementation and subsequent data analysis to document a full implementation cycle of changes. The implementation stage of introducing process improvements is critical in ensuring a system improvement is made and justifying the initial need for change. This research details a full implementation cycle of defining a problem through observation, identifying areas to change, selecting solutions to integrate and documenting implementation of solutions. This allows for benefits analysis over an extended time period to be conducted. A case study small manufacturing enterprise is used with a large reduction in their ‘design to delivery’ phase being the problem to address. Considerable analysis of key business functions has been completed through stakeholder input and time studies to identify all process bottlenecks. All bottlenecks identified have been scored based upon their impact to both the ‘design to delivery’ process time as well as to the stakeholder business plan. Comprehensive research was conducted on possible solutions to the key bottlenecks identified, with the most suitable solutions selected based upon a determined set of criteria unique to the company and the ‘design to delivery’ phase. In total 6 key bottlenecks were selected with 6 solutions chosen using the described methods. Significant benefits have been introduced to the case study company of a near 60% reduction in ‘design to delivery’ phase time, a new manufacturing process halving manufacturing costs and the introduction of automation. Introduction of new software packages have provided scope for further work opportunities to implement these elsewhere in the company. The research conducted has provided a unique study of implementing system improvements, building upon existing literature that typically concludes at the pre-implementation stage. Additionally, the combination of time study analysis and stakeholder input has allowed a company utilising traditional processes and typical characteristics of a small manufacturing enterprise to introduce considerable process changes, by removing any uncertainties or reluctance to do so. It is anticipated that the processes of the case study company are relatable to other small manufacturing enterprises looking to implement similar changes

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed

A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments

The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies. One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage. For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities. Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment. The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments. The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios. The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions. The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary